Once the degree of overfitting is assessed via cross-validation, structural or procedural adjustments must be made to the model. If there is minimal overfitting, it implies that the training data possesses enough complexity to support a more powerful model architecture. Conversely, if massive overfitting is detected—where the model perfectly fits the training set but performs poorly on validation folds—performance can typically be improved by incorporating regularization techniques rather than simply increasing model capacity.